Intelligent recommendation of time series anomaly detection model pipelines

The present invention relates generally to a time series anomaly detection method, and more particularly, but not by way of limitation, to a system, method, and computer program product to intelligently recommend time series anomaly detection model pipelines.

Detecting anomalous subsequences in time series data becomes more and more important and widely used in all walks of life. An anomaly can indicate momentous events, such as system defects, production faults, delivery bottlenecks, or heart flicker, and is therefore of central interest.

In an exemplary embodiment, the present invention can provide a computer-implemented time series anomaly detection method that processes time series data, the method including absorbing profiles of the time series data and anomaly types of a model as features, optimizing biased ranks to create optimized ranks through merging initial ranks with new ranks generated by real anomalies, and auto-suggesting the optimized ranks for saving a predetermined (large) amount of data operation.

In another exemplary embodiment, the present invention can provide a time series anomaly detection computer program product that processes time series data, the time series anomaly detection computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform: absorbing profiles of the time series data and anomaly types of a model as features, optimizing biased ranks to create optimized ranks through merging initial ranks with new ranks generated by real anomalies, and auto-suggesting the optimized ranks for saving a predetermined (large) amount of data operation.

In another exemplary embodiment, the present invention can provide a time series anomaly detection system that processes time series data, the time series anomaly detection system including a processor; and a memory, the memory storing instructions to cause the processor to perform: absorbing profiles of the time series data and anomaly types of a model as features, optimizing biased ranks to create optimized ranks through merging initial ranks with new ranks generated by real anomalies, and auto-suggesting the optimized ranks for saving a predetermined (large) amount of data operation.

In another exemplary embodiment, the present invention can include, based on the time series data, the initial ranks of the model are generated during the absorbing.

In another exemplary embodiment, the present invention can include that the profiles include profiling the time series data of the model and the anomaly types by generating synthesized anomaly data.

In another exemplary embodiment, the present invention can include during the optimizing, a relationship between the generated initial ranks and the profiled time series data of the model is stored.

In another exemplary embodiment, the optimized ranks are utilized for selecting top N models, where Nis an integer and can be set by a user.

Other details and embodiments of the invention will be described below, so that the present contribution to the art can be better appreciated. Nonetheless, the invention is not limited in its application to such details, phraseology, terminology, illustrations and/or arrangements set forth in the description or shown in the drawings.

Rather, the invention is capable of embodiments in addition to those described and of being practiced and carried out in various ways and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conception upon which this disclosure is based may readily be utilized as a basis for the designing of other structures, methods and systems for carrying out the several purposes (and others) of the present invention. It is important, therefore, that the claims be regarded as including such equivalent constructions insofar as they do not depart from the spirit and scope of the present invention.

1 9 FIGS.- The invention will now be described with reference to, in which like reference numerals refer to like parts throughout. It is emphasized that, according to common practice, the various features of the drawing are not necessarily to scale. On the contrary, the dimensions of the various features can be arbitrarily expanded or reduced for clarity.

200 2 FIG. With reference now to the exemplary methoddepicted in, the invention may include various steps for a time series anomaly detection technique that can improve models' selection performance and solve the bias problem of the synthesized ranking.

Conventional approaches for detecting anomalies include the utilization of semi-supervised methods. These conventional techniques involve constructing a model using normal data, allowing it to discern patterns representative of normalcy. Subsequently, the model proceeds to predict each new data point individually. Should any of these points deviate from the established normal pattern, it is classified as an anomaly.

However, during the model building phase, it is customary to create multiple models using normal data. These models are subsequently estimated using a dataset that encompasses both normal and randomly generated abnormal points that is usually synthesized against normal data for various anomaly types.

After that, the models that demonstrate the highest proficiency in detecting abnormal points, or anomalies, are chosen for further consideration.

Therefore, the conventional techniques have the problem that the time performance may be a big challenge particularly in a situation such as lots of candidate models with a bounty of anomaly types and model evaluation metrics. Another issue is that the model ranking based on the synthetic outliers may be biased. If so, then they should be optimized with the real rankings properly.

In view of this, the inventors have considered a technical solution to provide a time series anomaly detection technique that can improve models' selection performance and solve the bias problem of the synthesized ranking by absorbing profiles of time series data and anomaly types as features, optimizing biased ranks through merging initial ranks with the ones generated by real anomalies, auto-suggesting the optimized ranks for saving a predetermined (large) amount of data operations, and improving both speed and accuracy of models' selection by applying ranks auto-suggestions.

200 The time series anomaly detection methodaccording to an embodiment of the present invention may act in a more sophisticated, useful and cognitive manner, giving the impression of cognitive mental abilities and processes related to knowledge, attention, memory, judgment and evaluation, reasoning, and advanced computation. A system can be said to be “cognitive” if it possesses macro-scale properties—perception, goal-oriented behavior, learning/memory and action—that characterize systems (i.e., humans) generally recognized as cognitive.

Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.

A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.

1 9 FIGS.- 200 With reference generally to, by establishing and utilizing a relationship between time series data profiles and model ranks, a predetermined (i.e., large) number of data operations are reduced, and the model selection performance is greatly improved. Also, as a result of the method, accuracy of models' selection is improved by applying the optimized ranks for model evaluations. Further, as time series data and anomalies were involved in the model selection process, model interpretation was greatly improved and trust in the model was enhanced. And, because the invention utilized merging of synthetic ranks and real ranks, bias in model building process can be solved and/or reduced.

2 FIG. 200 With reference toand method, the invitation includes a time series anomaly detection method that can improve models' selection performance and solve the bias problem of the synthesized ranking.

201 In step, initial ranks of a model are generated by synthesized anomalies.

Ranks are a numerical value calculated from basic metrics to measure models' performance. Higher ranking values indicate better performance. Therefore, the ranking can be used to select the best model. It is noted that the term “initial” is used to distinguish the rank from the later “new” ranks.

In the context of anomaly detection, one may need to train and test anomaly detection algorithms on datasets that contain both normal and anomalous data. Sometimes, real-world anomalous data is scarce or sensitive, so one needs to create synthetic anomalies to augment the dataset for training and evaluation purposes.

There are many methods for generating synthesized anomalies. For example, the invention can use applying data transformations like Fourier or Wavelet transformations to the data and introduce anomalies in the transformed space, and then inverse-transform to obtain synthetic anomalies in the original data space.

202 In step, time series data and anomalies of a model are profiled as features (e.g., features refer to the variables or attributes used to represent the data that the model will learn from).

That is, profiles are absorbed of the time series data and anomaly types of a model as features.

In the context of the invention, anomalies refer to data points or instances that deviate significantly from the expected or “normal” behavior within a dataset. They are genuine observations that reflect unexpected or unusual behavior, errors, fraud, defects, or other noteworthy events in the real world.

Also, synthetic anomalies are artificially generated or manufactured anomalies that are intentionally added to a dataset. They are created for purpose to test and evaluate the performance of anomaly detection algorithms. These anomalies are not naturally occurring and are introduced for experimental or testing purposes.

203 202 7 FIG. In step, a relationship between the generated initial ranks using the synthesized anomalies and the profiled time series data using the anomalies are stored. For example, the profiled time series data are the data records of the features defined in step.exemplarily depicts a table of real examples of profiled time series data. The relationship is a kind of key-value pair where the key is profiles, and the value is the initial rank. Through the key, the value can be found.

204 201 204 8 FIG. c In step, new ranks are generated. Then, the generated initial ranks in stepare merged with the new ranks to create optimized ranks. The new rank is the rank generated from the process defined inin which one can see the real anomaly datais used there. “Optimized” for optimized ranks means the rank gets better compared to the initial rank because it is generated by merging the initial rank and new rank.

For example, biased ranks are optimized to create optimized ranks through merging initial ranks with new ranks generated by real anomalies.

205 In step, the optimized ranks are suggested for selecting top N models, with N being an integer. The optimized ranks can be auto-suggested for saving a predetermined (large) amount of data operation.

300 201 200 201 201 201 201 201 3 FIG. a b c f With reference to flowchartinthat depicts stepof methodin detail, in sub-step, time series data is input into step. In sub-step, time series anomaly detection (TSAD) models are built with the time series data. In sub-step, the TSAD models that are built are input into sub-stepdescribed later.

It is noted that time series anomaly detection (TSAD) is the process of identifying unusual or unexpected patterns, events, or data points within a time series dataset. Time series data incudes observations or measurements taken at sequential time intervals, thereby making it particularly valuable for tasks such as monitoring, forecasting, and anomaly detection in various domains, including finance, manufacturing, energy, and healthcare.

201 d Sub-stepincludes utilizing a synthesized anomaly generator. It is noted that the “synthesized anomaly generator” refers to structure that can perform the process to generate synthesized anomalies.

201 201 e f. In sub-step, the synthesized anomaly data generated by the synthesized anomaly generator is input into sub-step

201 201 201 f c e. In sub-step, the models are evaluated based on the TSAD models from sub-stepand the synthesized anomaly data from sub-step

Model evaluation is the process of assessing the performance and effectiveness of a model to determine how well it performs on a particular task or dataset. The goal of model evaluation is to understand how accurately the model makes predictions or classifications and to identify any potential issues or areas for improvement. It is a critical step in the development of machine learning models as it helps in selecting the best model, fine-tuning its parameters, and ensuring its suitability for the intended task.

Model evaluation is an iterative process, and it may involve refining the model, adjusting hyperparameters, or collecting additional data to improve performance. The choice of evaluation metrics and techniques should align with the specific goals and characteristics of the machine learning task.

201 201 400 g f 4 FIG. In sub-step, the models are ranked based on a result of the evaluation in sub-step. Rank examplesare depicted in.

201 201 h g In sub-step, the initial ranks of the model are generated based on a result of the ranking in sub-step. The initial ranks are based on the synthesized anomalies.

500 202 200 202 5 FIG. a With reference to flowchartinthat depicts stepof methodin detail, in sub-step, a data profile is generated via the time series features generator. Data profile refers to a summary or overview of a dataset's key characteristics, statistics, and properties.

600 6 FIG. To extract features that describe basic characteristics of the time series such as the number of peaks, the average or maximal value or more complex features such as the time reversal symmetry statistic may be used such as shown in elementof.

202 201 b e In sub-step, an anomaly profile is created based on the synthesized anomaly data generated in sub-step. The anomaly profile is a summary or description of the characteristics and properties of anomalies or outliers within a dataset.

700 7 FIG. That is, the anomaly profile includes pre-defined anomaly types that are incorporated as features, such as ‘LocalizedExtreme’, ‘LevelShift’, ‘Variance’, ‘Trend’, . . . , etc. (e.g., such as depicted in elementof).

202 201 201 c d c 3 204 FIGS.and 8 FIG. In sub-step, time series profile features are created based on the anomaly profile and the data profile. Time series profile features are based on actual anomalies with the data rather than the synthesized anomalies in step. For example, this is seen based on the different inputs toinin.

5 FIG. 203 203 As shown in, stepstores a relationship between the profiles including the time series profile features and the initial ranks. Therefore, the invention in stepstores a relationship between synthesized anomalies and actual anomalies with the time series data.

800 204 200 204 204 204 204 204 8 FIG. f h m n. With reference generally to flowchartinthat depicts stepof methodin detail, stepgenerates new ranksand merges the new rankswith the initial ranksto obtain optimized ranks

204 204 204 204 a c g. Stepincludes having labeled time series datawhich is utilized to extract real anomaly dataand to use within the time series features generator

204 204 204 204 204 c b d f The real anomaly datais used with the TSAD models(as discussed above) for model evaluationto determine model rankings. From the model rankings, new ranksare determined.

204 204 204 204 204 g i j i k The TS features generatorgenerates profile features. A profile similarity searchmay be performed based on the profile feature(s)that defines similarity strength using a technique like nearest neighbor, etc. The similarity search finds out the similar profile via a given one. These are input into the profiles vs. ranksdatabase. It is performed by the similarity search algorithm that is a computational technique used to find the most similar item in a dataset to a given query item or target item. It is noted that the search results are stored in a database for future use.

204 204 h To merge the ranks in step, stepdefines a score for merging ranks that is calculated based on an aggregation of the optimized metric (e.g., average precision) values for all supported anomaly types. The scores for each pipeline are ranked, using the Borda count method, and then weighted for their contribution to the aggregate score. Unlike a standard metric score, this value is not between 0 and 1. A higher value indicates a stronger score. Based on this, the initial and new ranks are merged as an optimized rank.

The new rankings obtained from the real anomalies are merged with the initial rankings generated by the synthetic anomalies, which can eliminate or attenuate the bias caused by the initial rankings.

900 205 200 205 9 FIG. With reference generally to flowchartinthat depicts stepof methodin detail, stepsuggest optimized ranks for selecting top N models quickly.

205 205 205 205 205 205 205 205 205 205 a a b c d e f j h. In step, new time series datais input to determine a model (top N models) to work with the new time series data. Using the time series data, the time series feature generatorgenerates profile featureswhich are then used in a profile similarity searchagainst the databasewith the profiles vs. ranks. From here, optimized ranksare output which can be used to determine top N modelsof the TSAD models

205 Stepcan include selecting top m models from n candidates where m can be a threshold determined by an end-user.

Exemplary Aspects, Using a Computing Environment

1 FIG. 100 200 200 100 101 102 103 104 105 106 101 110 120 121 111 112 113 122 200 114 123 124 125 115 104 130 105 140 141 142 143 144 With reference now to, computing environmentcontains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as time series anomaly detection code. In addition to block, computing environmentincludes, for example, computer, wide area network (WAN), end user device (EUD), remote server, public cloud, and private cloud. In this embodiment, computerincludes processor set(including processing circuitryand cache), communication fabric, volatile memory, persistent storage(including operating systemand block, as identified above), peripheral device set(including user interface (UI) device set, storage, and Internet of Things (IoT) sensor set), and network module. Remote serverincludes remote database. Public cloudincludes gateway, cloud orchestration module, host physical machine set, virtual machine set, and container set.

101 130 100 101 101 101 1 FIG. COMPUTERmay take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment, detailed discussion is focused on a single computer, specifically computer, to keep the presentation as simple as possible. Computermay be located in a cloud, even though it is not shown in a cloud in. On the other hand, computeris not required to be in a cloud except to any extent as may be affirmatively indicated.

110 120 120 121 110 110 PROCESSOR SETincludes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitrymay be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitrymay implement multiple processor threads and/or multiple processor cores. Cacheis memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor setmay be designed for working with qubits and performing quantum computing.

101 110 101 121 110 100 200 113 Computer readable program instructions are typically loaded onto computerto cause a series of operational steps to be performed by processor setof computerand thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cacheand the other storage media discussed below. The program instructions, and associated data, are accessed by processor setto control and direct performance of the inventive methods. In computing environment, at least some of the instructions for performing the inventive methods may be stored in blockin persistent storage.

111 101 COMMUNICATION FABRICis the signal conduction path that allows the various components of computerto communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up buses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.

112 112 101 112 101 101 VOLATILE MEMORYis any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, volatile memoryis characterized by random access, but this is not required unless affirmatively indicated. In computer, the volatile memoryis located in a single package and is internal to computer, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer.

113 101 113 113 122 200 PERSISTENT STORAGEis any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computerand/or directly to persistent storage. Persistent storagemay be a read only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data and re-writing of data. Some familiar forms of persistent storage include magnetic disks and solid state storage devices. Operating systemmay take several forms, such as various known proprietary operating systems or open source Portable Operating System Interface-type operating systems that employ a kernel. The code included in blocktypically includes at least some of the computer code involved in performing the inventive methods.

114 101 101 123 124 124 124 101 101 125 PERIPHERAL DEVICE SETincludes the set of peripheral devices of computer. Data communication connections between the peripheral devices and the other components of computermay be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion-type connections (for example, secure digital (SD) card), connections made through local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device setmay include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storageis external storage, such as an external hard drive, or insertable storage, such as an SD card. Storagemay be persistent and/or volatile. In some embodiments, storagemay take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computeris required to have a large amount of storage (for example, where computerlocally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor setis made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.

115 101 102 115 115 115 101 115 NETWORK MODULEis the collection of computer software, hardware, and firmware that allows computerto communicate with other computers through WAN. Network modulemay include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network moduleare performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network moduleare performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer readable program instructions for performing the inventive methods can typically be downloaded to computerfrom an external computer or external storage device through a network adapter card or network interface included in network module.

102 102 WANis any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments, the WANmay be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and edge servers.

103 101 101 103 101 101 115 101 102 103 103 103 END USER DEVICE (EUD)is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer), and may take any of the forms discussed above in connection with computer. EUDtypically receives helpful and useful data from the operations of computer. For example, in a hypothetical case where computeris designed to provide a recommendation to an end user, this recommendation would typically be communicated from network moduleof computerthrough WANto EUD. In this way, EUDcan display, or otherwise present, the recommendation to an end user. In some embodiments, EUDmay be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.

104 101 104 101 104 101 101 101 130 104 REMOTE SERVERis any computer system that serves at least some data and/or functionality to computer. Remote servermay be controlled and used by the same entity that operates computer. Remote serverrepresents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer. For example, in a hypothetical case where computeris designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computerfrom remote databaseof remote server.

105 105 141 105 142 105 143 144 141 140 105 102 PUBLIC CLOUDis any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloudis performed by the computer hardware and/or software of cloud orchestration module. The computing resources provided by public cloudare typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set, which is the universe of physical computers in and/or available to public cloud. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine setand/or containers from container set. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration modulemanages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gatewayis the collection of computer software, hardware, and firmware that allows public cloudto communicate through WAN.

Some further explanation of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.

106 105 106 102 105 106 PRIVATE CLOUDis similar to public cloud, except that the computing resources are only available for use by a single enterprise. While private cloudis depicted as being in communication with WAN, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloudand private cloudare both part of a larger hybrid cloud.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claim elements, and no amendment to any claim of the present application should be construed as a disclaimer of any interest in or right to an equivalent of any element or feature of the amended claim.